Kastra vs OpenObserve: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Kastra and OpenObserve — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Kastra

Kastra Labs Inc

Freemium

Kastra is the runtime authorization layer for AI: it decides in sub-milliseconds what agents, models, and tools are allowed to do before they act.

Sub-Millisecond Policy Decisions: Every prompt, tool call, and API request is evaluated in under a millisecond so enforcement never becomes the bottleneck.
Deterministic Policy Engine: Rules are written and evaluated deterministically, not by an LLM judge, so the same input always produces the same allow/deny.
Kastra Edge (Local Enforcement): A local enforcement component that runs next to the agent so decisions happen even without a network round-trip.
Cryptographic Audit Trails: Signed logs of every decision give security and compliance teams tamper-evident evidence of agent behavior.
Coding Agent Integrations: First-class hooks for Claude Code, Cursor, Codex, and OpenClaw let policies wrap the tool calls those agents already make.
Kastra Recon: Discovers what actions an agent actually attempts in a codebase or environment, so policies can be authored from observed behavior instead of guesses.
Zero Implicit Trust Model: Nothing an agent asks to do runs until it is explicitly allowed by policy, aligning agent access with zero-trust principles.

Guardrails for Coding Agents: Prevent an autonomous coding agent from dropping tables, force-pushing to main, or leaking secrets during long runs.
Enterprise Rollout Approvals: Give security teams a control plane before letting employee-facing AI agents access internal APIs.
Regulated-Industry Agent Deployments: Provide the auditable authorization trail required in finance, healthcare, or government agent pilots.
Multi-Agent Systems: Enforce per-agent scopes so a research agent can read data but only a deploy agent can trigger production changes.
Incident Forensics: Reconstruct exactly what an AI agent was allowed or blocked from doing after a suspicious action.

Freemium

Open-source unified observability for logs, metrics and traces, with an AI SRE agent that correlates signals and an LLM cost and eval monitor.

Unified Telemetry Store: Holds logs, metrics, traces, RUM, session replay and error tracking in a single system instead of separate tools per signal type.
Columnar Parquet Storage in Rust: Built on the DataFusion engine with no index to build, which underpins the claimed 140x storage and 30x compute reduction versus Elasticsearch.
Autocorrelation Engine: Continuously pairs signals across frontend, API, application, database, network and infrastructure layers at over a million signals per second.
AI SRE Agent: Investigates an incident by building a service graph, quantifying SLO and revenue impact, identifying the root cause from trace evidence, and applying a corrective action such as a rollback.
Proactive Daily Briefing: Reviews every service over a rolling 14-day window and flags the ones degrading, with the deploy or change that coincided with the regression.
Agentic and LLM Observability: Tracks token spend, per-model usage mix and error rates across models in production, with failed evaluations shown alongside prompt, output and grader score.
Transparent Usage Pricing: Charges per GB ingested and per GB queried with retention included, rather than tiered seat or host licensing.
Self-Hosted or Managed Cloud: The same platform can run entirely inside your own infrastructure or as a fully managed service, including BYOB for enterprise deployments.

Cutting Observability Spend: Replace an Elastic or Datadog deployment while keeping a year of log retention, using far less storage and compute for the same data.
Automated Incident Triage: Let the SRE agent correlate an error-rate spike to a specific deploy and propose the rollback before an engineer is paged.
Monitoring LLM Applications in Production: Track token cost, model mix and evaluation failures across several models serving live traffic.
Catching Slow Regressions: Surface a service whose p95 latency quietly tripled after an index rebuild, which threshold alerting would miss.
Full-Stack Root Cause Analysis: Trace a checkout failure from the browser through the API and into the database on one correlated timeline.
Compliance-Constrained Deployments: Self-host the whole observability stack so telemetry never leaves your own infrastructure.
SLO Management: Measure which service level objectives an ongoing incident is putting at risk and how much of a user flow is affected.